Application of Remote Sensing and Global Positioning Technology for Survey and Monitoring of Plant Pests David Bartels, Ph.D. USDA APHIS PPQ CPHST Mission Texas Laboratory
Spatial Technology and Plant Pests After a new pest is identified, the next question asked is: where is it located? Spatial Technology tools such as remote sensing (RS) and global positioning systems (GPS) can help with the survey, detection, and management of pests The data produce by these tools can be used by geographic information systems (GIS) for analysis
Outline Remote Sensing & GPS Applications Color Infrared Photography Giant Salvinia Biological Control Monitoring High-Resolution Digital Imagery Citrus Greening Survey Hyperspectral Imagery Saltcedar Biological Control Monitoring Emerald Ash Borer Host Detection
Remote Sensing Remote Sensing is defined as gathering information about an object from a distance For most pest detecting programs, remote sensing data comes from: satellite sensors airborne sensors Sensor types Mapping cameras (digital & film) Multispectral sensors Hyperspectal sensors
Advances in Remote Sensing Spatial resolution finer details Sensors have improved resolution down to sub-meter Spectral resolution improved classification Increased from 1 band (Panchromatic) to over 280 bands (Hyperspectral) 4m resolution 1m resolution
Ground Reference Data Global Positioning Systems have made collecting accurate spatial locations very easy Accurate ground reference data are critical for assessing the accuracy of remote sensing data and classifications
Giant Salvinia Biological Control Giant Salvinia is an invasive aquatic weed present in the US It is choking waterway in Texas, Louisiana, Arizona, and California
Giant Salvinia Biological Control The main approach to control has been the introduction of the Giant Salvinia weevil The weevil feeds on the roots of the plant and quickly causes dieback
Giant Salvinia Biological Control Wetland habitat makes monitoring difficult Color infrared aerial photography was use to document the spread of the salvinia weevil Image classification documented a 14% increase in severely damaged plants (red color) 3 months after release of the weevil
Citrus Greening (HLB) Survey The vector for citrus greening is currently present in Texas The disease has not be found, however, a survey in residential citrus trees will take place this summer Survey with cover 96 square miles
Citrus Greening Survey 2007 high resolution color imagery is available for the area (6 inch) We tested automated feature extraction methods (Feature Analyst Software) Locate small citrus in urban areas Ground reference data was collected using GPS for training sites
Citrus Greening Survey Automated feature extraction was not able different citrus trees from all other tree species
Citrus Greening Survey However, the resolution of the imagery is fine enough to allow manual photo interpretation to identify tree species Goal is to reduce the number of residence that ground survey personnel need to visit
Saltcedar Biological Control Monitoring Saltcedar was first introduced to the U.S. in the 1800s Primarily found along riparian zones in the southwest region of the U.S. Major impacts include: Increased soil salinity Increased water consumption Increased wildfire hazard Develops thick monoculture stands forcing out native vegetation
Saltcedar Biological Control Monitoring Dorhabda elongata has been cleared for release Experimental releases in 6 western states Causes major defoliation to saltcedar plants and plants are being monitored for re-growth potential and loss of reproductive capacity
Saltcedar Biological Control Monitoring Data Type Hyperspectral imagery captured using a Cessna Skymaster 337 hosting an ITRES CASI 2 sensor Data Resolution 1-22 meter spatial resolution 37-49 bands (420-954 nms), each band ~7 nm wide
Saltcedar Distribution Mapping Successfully delineated saltcedar at the study site for all 3 years data Overall saltcedar distribution 2002 = 87.58 acres (79.03%*) 2003 = 94.23 acres (95.24%*) 2004 = 86.39 acres (84.13%*)
Emerald Ash Borer Host Detection EAB was first identified near Detroit, MI in 2002 Probably present since mid 1990s Imported from Asia Solid wood packing material Photo by David Cappaert
Extent of EAB Infestation
EAB Damage & Symptoms Foliage dies back beginning with the crown of the tree Epicormic shoots often appear on dying trees Bark splits Tree is usually dead in 2-42 4 years
Current Survey Methods Sentinal Trap Trees Girdling Peeling at end of season Trapping Purple sticky traps Visual Survey A study was developed to test hyperspectral remote sensing
Main Question Can we use remote sensing technology to produce an accurate map of ash tree locations and health status over a large area?
2006 Airborne Data Collection Sanborn general contractor Panchromatic Imagery 0.25m spatial resolution LiDAR data 0.5m spatial resolution Hyperspectral Imagery 1m spatial resolution in the visible and near-infrared bands (SpecTIR( SpecTIR s AISA Eagle sensor) 1 st 2 nd st collection in June nd collection in late August
2006 Flight Area Locations 150 km 2 Northern Michigan Sites Boyne City Petoskey Central Michigan Sites Ann Arbor Napoleon Brooklyn Ohio Sites Toledo Oak Openings Grand Rapids
Analysis Groups Sanborn Remote Measurement Services (RMS) USDA FS & University of New Hampshire Clark Labs (Clark University) ITT Space Systems Division
RMS Analysis Hyperspectal Pixel Analysis RMS has conducted a pixel level analysis on both sets of hyperspectral data
Hyperspectral Pixel Analysis Results - June GBC-Health Scores
Hyperspectral Pixel Analysis Results - August GBC-Health Scores Ann Arbor flight area Training Data # Trees Pixels Correct Trees Correct Green H=1 4 71.10% 75.00% White H=1 6 77.20% 66.70% H=2 4 72.30% 100.00% H=3 5 76.30% 100.00% H=4 7 60.00% 83.30% H=5 3 100.00% 100.00% Verification Data Green H=1 5 71.40% 80.00% White H=1 1 85.70% 100.00% H=2 4 26.90% 50.00% H=3 5 47.20% 40.00% H=4 4 37.60% 25.00% H=5 3 41.70% 66.70%
Hyperspectral Pixel Analysis Results - June GBC-Health Scores Average across all flight area Training Data # Trees Pixels Correct Min Max Trees Correct Min Max Green H=1 88 60.80% 33.50% 82.10% 63.00% 38.50% 75.00% White H=1 28 58.28% 0.00% 97.10% 47.56% 0.00% 100.00% Black H=1 1 78.60% 78.60% 78.60% 100.00% 100.00% 100.00% H=2 60 57.44% 13.80% 94.90% 52.11% 0.00% 100.00% H=3 21 58.26% 0.00% 78.10% 58.34% 0.00% 100.00% H=4 15 74.40% 61.50% 93.80% 87.77% 80.00% 100.00% H=5 7 85.23% 75.80% 100.00% 100.00% 100.00% 100.00% Verification Data Green H=1 84 36.59% 7.70% 61.30% 38.81% 0.00% 70.00% White H=1 18 66.30% 46.70% 100.00% 69.03% 50.00% 100.00% Black H=1 0 H=2 38 39.05% 0.00% 67.70% 33.46% 0.00% 70.00% H=3 29 42.80% 9.70% 76.20% 50.63% 0.00% 100.00% H=4 15 30.03% 18.00% 48.50% 8.33% 0.00% 25.00% H=5 8 49.93% 0.00% 87.50% 66.67% 0.00% 100.00%
Further Analysis - EAB Complete the accuracy assessment for the hyperspectral pixel analysis on ash health and non ash Determine the effect of distance on training data Get results back from other analysis groups
Conclusions Spatial technology could be incorporated into almost every pest detection program GPS systems should be used to collect spatial location on pest survey data Aerial imagery is very useful in pest detection and survey programs Helps focus survey efforts and save resources Hyperspectral imaging technology holds great promise for improving pest survey and detection The challenge is moving the technology into an operational context. Remote sensing and global positioning technology can play a key role in the detection of many invasive pests
Acknowledgements Giant Salvinia Daniel Flores USDA APHIS PPQ Jim Everitt USDA ARS Citrus Greening Rich Somers USDA APHIS PPQ Stephen Tice LRGVDC Saltcedar Lisa Kennaway USDA APHIS PPQ Gerry Anderson USDA ARS Emerald Ash Borer Russell Sheetz USDA APHIS PPQ David Williams USDA APHIS PPQ David Cappaert Michigan State Univ. Deb McCullough Michigan State Univ.
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